scholarly journals Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations

2018 ◽  
Author(s):  
Yang Luo ◽  
Xinyi Li ◽  
Xin Wang ◽  
Steven Gazal ◽  
Josep Maria Mercader ◽  
...  
Keyword(s):  
African American ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Data ◽  
Age At Menarche ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
Tissue Specific ◽  
Different Populations

AbstractThe increasing size and diversity of genome-wide association studies provide an exciting opportunity to study how the genetics of complex traits vary among diverse populations. Here, we introduce covariate-adjusted LD score regression (cov-LDSC), a method to accurately estimate genetic heritability and its enrichment in both homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the GWAS samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates by 10% − 60% in admixed populations; in contrast, cov-LDSC is robust to all simulation parameters. We apply cov-LDSC to genotyping data from approximately 170,000 Latino, 47,000 African American and 135,000 European individuals. We estimate and detect heritability enrichment in three quantitative and five dichotomous phenotypes respectively, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals. Our results show that most traits have high concordance of and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of . We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size τ* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in Latino, African American and European populations respectively. Our results demonstrate that our approach is a powerful way to analyze genetic data for complex traits from underrepresented populations.Author summaryAdmixed populations such as African Americans and Hispanic Americans bear a disproportionately high burden of disease but remain underrepresented in current genetic studies. It is important to extend current methodological advancements for understanding the genetic basis of complex traits in homogeneous populations to individuals with admixed genetic backgrounds. Here, we develop a computationally efficient method to answer two specific questions. First, does genetic variation contribute to the same amount of phenotypic variation (heritability) across diverse populations? Second, are the genetic mechanisms shared among different populations? To answer these questions, we use our novel method to conduct the first comprehensive heritability-based analysis of a large number of admixed individuals. We show that there is a high degree of concordance in total heritability and tissue-specific enrichment between different ancestral groups. However, traits such as age at menarche show a noticeable differences among populations. Our work provides a powerful way to analyze genetic data in admixed populations and may contribute to the applicability of genomic medicine to admixed population groups.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases

2021 ◽  
Author(s):  
Gui-Juan Feng ◽  
Qian Xu ◽  
Jing-Jing Ni ◽  
Shan-Shan Yang ◽  
Bai-Xue Han ◽  
...  
Keyword(s):  
Association Study ◽  
Complex Traits ◽  
Causal Effect ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Age At Menarche ◽  
Luteinizing Hormone Level ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 participants from 3 genome-wide association studies of European and East Asian ancestries. Twenty-one novel genomic loci were identified at the genome-wide significance level. Besides, we observed significant genetic correlations between AAM and 67 complex traits, and the highest genetic correlation was observed between AAM and body mass index (rg=-0.19, P=6.11×10−31). Latent causal variable analyses demonstrate that there is a genetically causal effect of AAM on high blood pressure (GCP=0.47, P=0.02), forced vital capacity (GCP=0.63, P=0.02), age at first live birth (GCP=0.51, P=0.03), impedance of right arm (GCP=0.41, P<1×10-7) and right leg fat percentage (GCP=-0.10, P=0.02), etc. Enrichment analysis identified 5 enriched tissues and 51 enriched gene sets. Four of the five enriched tissues were related to the nervous system, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems and hypothalamus. The fifth tissue was the retina in the sensory organ. The most significant gene set was the ‘decreased circulating luteinizing hormone level’ (P=2.45×10-6). Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.

scholarly journals Large-scale cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets

2017 ◽  
Author(s):  
Max Lam ◽  
Joey W. Trampush ◽  
Jin Yu ◽  
Emma Knowles ◽  
Gail Davies ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Correlations ◽  
Adult Brain ◽  
Specific Gene ◽  
Age At First Birth ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Brain Expression

AbstractNeurocognitive ability is a fundamental readout of brain function, and cognitive deficits are a critical component of neuropsychiatric disorders, yet neurocognition is poorly understood at the molecular level. In the present report, we present the largest genome-wide association studies (GWAS) of cognitive ability to date (N=107,207), and further enhance signal by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with cognitive ability, 34 of which were novel. A total of 350 genes were implicated, and this list showed significant enrichment for genes associated with Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis of gene results implicated the biological process of neurogenesis, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker; and LY97241, a potassium channel inhibitor. Transcriptome-wide analysis revealed that the implicated genes were strongly expressed in neurons, but not astrocytes or oligodendrocytes, and were more strongly associated with fetal brain expression than adult brain expression. Several tissue-specific gene expression relationships to cognitive ability were observed (for example, DAG1 levels in the hippocampus). Finally, we report novel genetic correlations between cognitive ability and disparate phenotypes such as maternal age at first birth and number of children, as well as several autoimmune disorders.

scholarly journals Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

2020 ◽  
Author(s):  
Yanyu Liang ◽  
François Aguet ◽  
Alvaro Barbeira ◽  
Kristin Ardlie ◽  
Hae Kyung Im
Keyword(s):  
Gene Expression ◽  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Average Power ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Causal Genes ◽  
Allele Specific

AbstractGenome-wide association studies (GWAS) have been highly successful in identifying genomic loci associated with complex traits. However, identification of the causal genes that mediate these associations remains challenging, and many approaches integrating transcriptomic data with GWAS have been proposed. However, there currently exist no computationally scalable methods that integrate total and allele-specific gene expression to maximize power to detect genetic effects on gene expression. Here, we describe a unified framework that is scalable to studies with thousands of samples. Using simulations and data from GTEx, we demonstrate an average power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and prediction.

scholarly journals Big data screening identifies 110 complex traits causally associated with obesity

2020 ◽  
Author(s):  
Luis M. García-Marín ◽  
Adrián I. Campos ◽  
Pik-Fang Kho ◽  
Nicholas G. Martin ◽  
Gabriel Cuéllar-Partida ◽  
...  
Keyword(s):  
Complex Traits ◽  
Metabolic Diseases ◽  
Association Studies ◽  
Neurodevelopmental Disorder ◽  
Genetic Data ◽  
Health Concern ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Variable ◽  
Data Screening

ABSTRACTBackground/ObjectivesObesity has become a serious public health concern worldwide due to the rapid increase in its prevalence and its multiple negative health consequences. Here we sought to identify causal relationships between obesity and other complex traits and conditions using a data-driven hypothesis-free approach that relies on genetic data to infer causal associations.Subjects/MethodsWe leveraged available summary-based genetic data from genome-wide association studies on 1 498 phenotypes and applied the latent causal variable method (LCV) between obesity and all traits.ResultsWe identified 110 traits with significant causal associations with obesity. Results show obesity influencing 26 phenotypes associated with cardiovascular diseases, 22 anthropometric measurements, 9 with the musculoskeletal system, 9 with behavioural or lifestyle factors including loneliness or isolation, 6 with respiratory diseases, 5 with body bioelectric impedances, 4 with psychiatric phenotypes, 4 with the nervous system, 4 with disabilities or long-standing illness, 3 with the gastrointestinal system, 3 with use of analgesics, 2 with metabolic diseases such as diabetes, 1 with inflammatory response and 1 with the neurodevelopmental disorder ADHD, among others.ConclusionsOur results indicate that obesity is primarily the cause, not the consequence of other underlying traits or comorbid diseases. The wide array of causally associated phenotypes provides an overview of the metabolic, physiological, and neuropsychiatric impact of obesity.

scholarly journals Leveraging biobank-scale rare and common variant analyses to identify ASPHD1 as the main driver of reproductive traits in the 16p11.2 locus

2019 ◽  
Author(s):  
Katrin Männik ◽  
Thomas Arbogast ◽  
Maarja Lepamets ◽  
Kaido Lepik ◽  
Anna Pellaz ◽  
...  
Keyword(s):  
Complex Traits ◽  
Reproductive Tract ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Interdisciplinary Approach ◽  
Copy Number Variant ◽  
Age At Menarche ◽  
Genome Wide Association Studies ◽  
Pubertal Onset ◽  
Causal Genes

AbstractWhereas genome-wide association studies (GWAS) allowed identifying thousands of associations between variants and traits, their success rate in pinpointing causal genes has been disproportionately low. Here, we integrate biobank-scale phenotype data from carriers of a rare copy-number variant (CNV), Mendelian randomization and animal modeling to identify causative genes in a GWAS locus for age at menarche (AaM). We show that the dosage of the 16p11.2 BP4-BP5 interval is correlated positively with AaM in the UK and Estonian biobanks and 16p11.2 clinical cohorts, with a directionally consistent trend for pubertal onset in males. These correlations parallel an increase in reproductive tract disorders in both sexes. In support of these observations, 16p11.2 mouse models display perturbed pubertal onset and structurally altered reproductive organs that track with CNV dose. Further, we report a negative correlation between the 16p11.2 dosage and relative hypothalamic volume in both humans and mice, intimating a perturbation in the gonadotropin-releasing hormone (GnRH) axis. Two independent lines of evidence identified candidate causal genes for AaM; Mendelian randomization and agnostic dosage modulation of each 16p11.2 gene in zebrafish gnrh3:egfp models. ASPHD1, expressed predominantly in brain and pituitary gland, emerged as a major phenotype driver; and it is subject to modulation by KCTD13 to exacerbate GnRH neuron phenotype. Together, our data highlight the power of an interdisciplinary approach to elucidate disease etiologies underlying complex traits.

scholarly journals A general framework for predicting the transcriptomic consequences of non-coding variation

2018 ◽  
Author(s):  
Moustafa Abdalla ◽  
Mohamed Abdalla ◽  
Mark I. McCarthy ◽  
Chris C. Holmes
Keyword(s):  
Complex Traits ◽  
Linear Models ◽  
Association Studies ◽  
Transcript Abundance ◽  
Evolutionary Constraint ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genotype Variation ◽  
Functional Understanding ◽  
Allele Specific

ABSTRACTGenome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regularized linear models in predicting the consequences of individual genotype variation. We highlight the validity of the peaBrain model by calculating non-coding impact scores that correlate with nucleotide evolutionary constraint that are also predictive of disease-associated variation and allele-specific transcription factor binding. We further show how these tissue-specific peaBrain scores can be leveraged to pinpoint functional tissues underlying complex traits, outperforming methods that depend on colocalization of eQTL and GWAS signals. We subsequently derive continuous dense embeddings of genes for downstream applications, and identify putatively functional eQTLs that are missed by high-throughput experimental approaches.

scholarly journals Identifying causal variants by fine mapping across multiple studies

PLoS Genetics ◽  
2021 ◽  
Vol 17 (9) ◽  
pp. e1009733
Author(s):  
Nathan LaPierre ◽  
Kodi Taraszka ◽  
Helen Huang ◽  
Rosemary He ◽  
Farhad Hormozdiari ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Multivariate Normal ◽  
Multiple Study ◽  
Genome Wide ◽  
Causal Variants ◽  
Different Populations

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).

scholarly journals Tissue-specific 5-hydroxymethylcytosine landscape of the human genome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bo He ◽  
Chao Zhang ◽  
Xiaoxue Zhang ◽  
Yu Fan ◽  
Hu Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Genome ◽  
Association Studies ◽  
Specific Gene ◽  
Epigenetic Mark ◽  
Genome Wide Association Studies ◽  
Human Tissues ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

Abstract5-Hydroxymethylcytosine (5hmC) is an important epigenetic mark that regulates gene expression. Charting the landscape of 5hmC in human tissues is fundamental to understanding its regulatory functions. Here, we systematically profiled the whole-genome 5hmC landscape at single-base resolution for 19 types of human tissues. We found that 5hmC preferentially decorates gene bodies and outperforms gene body 5mC in reflecting gene expression. Approximately one-third of 5hmC peaks are tissue-specific differentially-hydroxymethylated regions (tsDhMRs), which are deposited in regions that potentially regulate the expression of nearby tissue-specific functional genes. In addition, tsDhMRs are enriched with tissue-specific transcription factors and may rewire tissue-specific gene expression networks. Moreover, tsDhMRs are associated with single-nucleotide polymorphisms identified by genome-wide association studies and are linked to tissue-specific phenotypes and diseases. Collectively, our results show the tissue-specific 5hmC landscape of the human genome and demonstrate that 5hmC serves as a fundamental regulatory element affecting tissue-specific gene expression programs and functions.

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